Update app.py

app.py

@@ -125,55 +125,54 @@ class DicomAnalyzer:
             if self.current_image is None:
                 return None, "No image loaded"
 
-            # Get the clicked coordinates
+            # Get raw image data before any processing
+            raw_image = self.current_image.copy()
+            
+            # Get clicked coordinates and adjust for zoom/pan
             clicked_x = evt.index[0]
             clicked_y = evt.index[1]
-
-            # Adjust for pan and zoom
+            
+            # Convert to original image coordinates
             x = int((clicked_x + self.pan_x) / self.zoom_factor)
             y = int((clicked_y + self.pan_y) / self.zoom_factor)
 
-            # Ensure coordinates are within image bounds
-            height, width = self.current_image.shape[:2]
-            x = max(0, min(x, width-1))
-            y = max(0, min(y, height-1))
-
-            # Create mask for ROI
-            mask = np.zeros_like(self.current_image, dtype=np.uint8)
-            y_indices, x_indices = np.ogrid[:self.current_image.shape[0], :self.current_image.shape[1]]
+            # Create mask for ROI using raw image dimensions
+            mask = np.zeros_like(raw_image, dtype=np.uint8)
+            y_indices, x_indices = np.ogrid[:raw_image.shape[0], :raw_image.shape[1]]
             radius = self.circle_diameter / 2
             distance_from_center = np.sqrt(
                 (x_indices - x)**2 + (y_indices - y)**2
             )
             mask[distance_from_center <= radius] = 1
 
-            # Calculate statistics
-            roi_pixels = self.current_image[mask == 1]
+            # Get ROI pixels from raw image
+            roi_pixels = raw_image[mask == 1]
+
+            # Calculate statistics from raw pixel values
             pixel_spacing = float(self.dicom_data.PixelSpacing[0])
             area_pixels = np.sum(mask)
             area_mm2 = area_pixels * (pixel_spacing ** 2)
+            
+            # Calculate statistics without any normalization
             mean = np.mean(roi_pixels)
             stddev = np.std(roi_pixels)
             min_val = np.min(roi_pixels)
             max_val = np.max(roi_pixels)
 
-            # Adjust coordinates to match ImageJ coordinate system
-            # ImageJ coordinates start from top-left
-            imagej_x = x
-            imagej_y = y
-
             result = {
                 'Area (mm²)': f"{area_mm2:.3f}",
                 'Mean': f"{mean:.3f}",
                 'StdDev': f"{stddev:.3f}",
                 'Min': f"{min_val:.3f}",
                 'Max': f"{max_val:.3f}",
-                'Point': f"({imagej_x}, {imagej_y})"
+                'Point': f"({x}, {y})"
             }
             
             self.results.append(result)
             self.marks.append((x, y, self.circle_diameter))
-            print(f"ROI analyzed at point ({imagej_x}, {imagej_y})")
+            print(f"ROI analyzed at point ({x}, {y})")
+            print(f"Mean: {mean:.3f}, StdDev: {stddev:.3f}")
+            print(f"Min: {min_val:.3f}, Max: {max_val:.3f}")
 
             return self.update_display(), self.format_results()
         except Exception as e: